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82f0ce2726
This provide several advantages: - more flexibility in designing unit tests - unit tests can be glued to speed up compilation - unit tests are compiled with same predefined macros, which is a requirement for zapcc
153 lines
3.7 KiB
C++
153 lines
3.7 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#include "main.h"
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#include <Eigen/CXX11/Tensor>
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using Eigen::Tensor;
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using Eigen::TensorMap;
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static void test_assign()
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{
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std::string data1[6];
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TensorMap<Tensor<std::string, 2>> mat1(data1, 2, 3);
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std::string data2[6];
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const TensorMap<Tensor<const std::string, 2>> mat2(data2, 2, 3);
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for (int i = 0; i < 6; ++i) {
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std::ostringstream s1;
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s1 << "abc" << i*3;
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data1[i] = s1.str();
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std::ostringstream s2;
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s2 << "def" << i*5;
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data2[i] = s2.str();
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}
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Tensor<std::string, 2> rslt1;
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rslt1 = mat1;
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Tensor<std::string, 2> rslt2;
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rslt2 = mat2;
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Tensor<std::string, 2> rslt3 = mat1;
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Tensor<std::string, 2> rslt4 = mat2;
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Tensor<std::string, 2> rslt5(mat1);
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Tensor<std::string, 2> rslt6(mat2);
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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VERIFY_IS_EQUAL(rslt1(i,j), data1[i+2*j]);
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VERIFY_IS_EQUAL(rslt2(i,j), data2[i+2*j]);
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VERIFY_IS_EQUAL(rslt3(i,j), data1[i+2*j]);
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VERIFY_IS_EQUAL(rslt4(i,j), data2[i+2*j]);
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VERIFY_IS_EQUAL(rslt5(i,j), data1[i+2*j]);
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VERIFY_IS_EQUAL(rslt6(i,j), data2[i+2*j]);
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}
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}
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}
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static void test_concat()
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{
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Tensor<std::string, 2> t1(2, 3);
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Tensor<std::string, 2> t2(2, 3);
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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std::ostringstream s1;
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s1 << "abc" << i + j*2;
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t1(i, j) = s1.str();
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std::ostringstream s2;
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s2 << "def" << i*5 + j*32;
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t2(i, j) = s2.str();
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}
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}
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Tensor<std::string, 2> result = t1.concatenate(t2, 1);
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VERIFY_IS_EQUAL(result.dimension(0), 2);
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VERIFY_IS_EQUAL(result.dimension(1), 6);
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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VERIFY_IS_EQUAL(result(i, j), t1(i, j));
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VERIFY_IS_EQUAL(result(i, j+3), t2(i, j));
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}
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}
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}
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static void test_slices()
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{
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Tensor<std::string, 2> data(2, 6);
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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std::ostringstream s1;
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s1 << "abc" << i + j*2;
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data(i, j) = s1.str();
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}
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}
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const Eigen::DSizes<ptrdiff_t, 2> half_size(2, 3);
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const Eigen::DSizes<ptrdiff_t, 2> first_half(0, 0);
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const Eigen::DSizes<ptrdiff_t, 2> second_half(0, 3);
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Tensor<std::string, 2> t1 = data.slice(first_half, half_size);
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Tensor<std::string, 2> t2 = data.slice(second_half, half_size);
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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VERIFY_IS_EQUAL(data(i, j), t1(i, j));
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VERIFY_IS_EQUAL(data(i, j+3), t2(i, j));
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}
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}
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}
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static void test_additions()
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{
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Tensor<std::string, 1> data1(3);
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Tensor<std::string, 1> data2(3);
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for (int i = 0; i < 3; ++i) {
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data1(i) = "abc";
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std::ostringstream s1;
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s1 << i;
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data2(i) = s1.str();
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}
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Tensor<std::string, 1> sum = data1 + data2;
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for (int i = 0; i < 3; ++i) {
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std::ostringstream concat;
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concat << "abc" << i;
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std::string expected = concat.str();
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VERIFY_IS_EQUAL(sum(i), expected);
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}
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}
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static void test_initialization()
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{
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Tensor<std::string, 2> a(2, 3);
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a.setConstant(std::string("foo"));
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for (int i = 0; i < 2*3; ++i) {
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VERIFY_IS_EQUAL(a(i), std::string("foo"));
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}
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}
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EIGEN_DECLARE_TEST(cxx11_tensor_of_strings)
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{
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// Beware: none of this is likely to ever work on a GPU.
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CALL_SUBTEST(test_assign());
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CALL_SUBTEST(test_concat());
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CALL_SUBTEST(test_slices());
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CALL_SUBTEST(test_additions());
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CALL_SUBTEST(test_initialization());
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}
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